Presently, a large number of businesses maintain one or more databases to support a variety of processes, such as processes involving the manufacture and delivery of products, or the provision of particular services. It goes without saying, then, that the data that are stored in a database needs to be accurate to ensure that the processes which rely on such data arrive at a correct result. For example, if the quality of data that are stored in a database supporting the manufacture and delivery of products is poor, then it is likely that an aspect of such manufacturing and/or delivery may also be poor.
Heretofore, the problem of maintaining the quality of data that is stored in a database has been dealt with by inspecting, in a conventional manner, the stored data and correcting errors as they are encountered. However, such an error correcting process is not only time-consuming, but is also very expensive. In addition, such an error correcting technique does not result in a sustainable improvement of the quality of data stored in a database, since the technique must be performed each time the stored data changes. Thus, despite its importance in the operation of a business, little attention has been directed toward improving and maintaining the quality of data that is stored in a database.